Bibliographic record
Abstract
The IEEE Industry Applications Society (IAS) Chapter and Membership Department (CMD) organizes the Distinguished Lecturer and Prominent Lecturer Program (DL/PL). The program provides IAS chapters the opportunity to invite and host lecturers by experts in their field of interest. These lectures can be in person or virtual, presenting on a topic within the area of expertise of the invited DL. The program offers a list of renowned, exceptional lecturers who each possess specialist knowledge within their field. In addition to their technical expertise, the lecturers are also longtime IAS volunteers. The DL/PL programs can be hosted by Chapters in noncommercial technical, leadership, or combined meetings and workshops of IAS single or joint Chapters as well as IAS Student Branch Chapters. The DL/PL program’s invited members should not be charged a registration fee, other than the fee charged for a meal during the event. The DL and PL presentations should be initiated by the local Chapter chair. The inviting Chapter chair should make preliminary arrangements directly with the lecturer through introductory correspondence and come to an agreement on the date, time, venue, and so on. Having decided, the invitee will submit a completed DL/PL travel request approval form to the DL program committee chair. Detailed instructions can be found on the IAS website athttps://ias.ieee.org/member-development/distinguished-lecturer-program/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.572 | 0.644 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".